Network real estate analysis

ABSTRACT

A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of and claims a benefit of priority under 35U.S.C. §120 of the filing date of U.S. patent application Ser. No.12/684,609, entitled “NETWORK REAL ESTATE ANALYSIS” by Brendan J. Kitts,filed Jan. 8, 2010 issued as U.S. Pat. No. 8,024,448 on Sep. 20, 2011,which is a continuation of U.S. patent application Ser. No. 10/202,742,entitled “NETWORK REAL ESTATE ANALYSIS” by Brendan J. Kitts, filed Jul.25, 2002, issued as U.S. Pat. No. 7,660,869 on Feb. 9, 2010, which inturn claims a benefit of priority under 35 U.S.C. §120 and is acontinuation-in-part of U.S. patent application Ser. No. 09/934,415,entitled “A SYSTEM AND METHOD FOR GRAPHICALLY ANALYZING PRODUCTINTERACTIONS” by Brendan J. Kitts, filed Aug. 21, 2001, now abandoned,which in turn claims priority under 35 U.S.C. §119(e) to U.S.Provisional Patent Application Nos. 60/308,075, entitled “VISUALIZATIONAND ANALYSIS OF USER CLICKPATHS” by Brendan J. Kitts, filed Jul. 26,2001, and 60/226,798, entitled “METHOD AND SYSTEM FOR GRAPHICALLYREPRESENTING CUSTOMER AFFINITIES” by Brendan J. Kitts, filed Aug. 21,2000. This application is also related to U.S. patent application Ser.No. 10/202,741, filed Jul. 25, 2002, issued as U.S. Pat. No. 7,278,105on Oct. 2, 2007, entitled “VISUALIZATION AND ANALYSIS OF USERCLICKPATHS” by Brendan J. Kitts. All applications listed in thisparagraph are fully incorporated herein by reference.

TECHNICAL FIELD

This invention relates in general to methods and data processing systemreadable storage media, and more particularly, to methods of analyzingperformance of content within network sites and data processing systemreadable storage media having software code for carrying out thosemethods.

DESCRIPTION OF THE RELATED ART

The placement of content on a website can be the difference between asuccessful and an unproductive web site. Previous work has not yetrevealed an ideal method for placing content. For example, eye trackershave been used by some researchers to examine what attracts the eye todifferent features on a page. Perhaps this could be used to move higherrevenue advertisements into more attractive page locations. In differentwork, Huberman et al. (1998) found that the probability of a web surferremaining on a site declines with each additional click. Perhaps thismight indicate that high revenue content could be placed near the entrypages of the site. However, whilst both are interesting concepts, theydo not on their own provide a way for optimizing site layout. A generalpurpose method is needed that can help determine where content should beplaced in a web site so as to maximize site performance.

SUMMARY OF THE DISCLOSURE

Methods and data processing system readable storage media have beencreated to analyze the “real-estate performance” of a content itemwithin a network site.

In one set of embodiments, a data processing system readable storagemedium can have code embodied therein, and the code can be used toanalyze the performance of content item(s) within a network site. Thecode can comprise an instruction for determining a location of a contentitem within a network site. The code can also comprise an instructionfor determining a predicted performance associated with the contentitem. The predicted performance may be a function of the location of thecontent item. The code can further comprise an instruction for comparingthe predicted number and an actual number for the performance statisticfor the content item. The method may also comprise moving the contentitem to a different location.

In another set of embodiments, the code can comprise an instruction fordetermining locations of content items within a network site. The codecan also comprise an instruction for generating a graph includinginformation related to the locations and performance of content items.

The code can be described with respect to activities performed as amethod. While the use of a computer program facilitates the use of themethod, at least some of the acts used in the method may be performed byhuman(s). For example, determining whether to move the location ofcontent and where may be better performed by a human. The foregoinggeneral description and the following detailed description are exemplaryand explanatory only and are not restrictive of the invention, asdefined in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the accompanying figures, in which:

FIG. 1 includes an illustration of a client computer and a servercomputer as part of a computer network.

FIG. 2 includes an illustration of a data processing system storagemedium including software code having instructions in accordance with anembodiment of the present invention.

FIGS. 3 and 4 include process flow diagrams for analyzing performance ofcontent item(s) within a network site.

FIGS. 5 and 6 include examples of data that may be collected within anetwork site log.

FIG. 7 includes a graph illustrating navigation via different clickpathsto a content item within a network site.

FIG. 8 includes a graph illustrating content items as a function oflocation within the network site.

FIG. 9 includes a graph illustrating a relationship between traffic andclick distance for content items.

FIGS. 10 and 11 include illustrations of tables with content items withthe highest ratios and lowest ratios of “hits/E[hits]”.

Skilled artisans appreciate that elements in the figures are illustratedfor simplicity and clarity and have not necessarily been drawn to scale.For example, the dimensions of some of the elements in the figures maybe exaggerated relative to other elements to help to improveunderstanding of embodiments of the present invention.

DETAILED DESCRIPTION

Reference is now made in detail to the exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings.

Overview of the Methodology

The embodiments shown in FIGS. 3 and 4 can be used to analyze thereal-estate performance of a content item within a network site.

In the embodiment shown in FIG. 3, the method can comprise obtaining anetwork site log (block 302), determining locations of content itemswithin the network site (block 322), and the actual and predictedperformance for each content item (block 324). The method may furthercomprise comparing the predicted and actual performances (block 342).The method can also comprise moving at least one content item to adifferent network address (block 362).

In an alternative embodiment shown in FIG. 4, the method can compriseobtaining a network site log (block 302), determining locations forcontent items within the network site (block 322) and accessing theactual performance of the content items (block 424). The method canstill further comprise generating a graph of actual performance versuslocation for the content items (block 442), fitting a curve to the graph(block 444), and reviewing the graph (block 446). The method cancomprise moving at least one of the content items to a different networkaddress (block 462).

Clarification of Terms

The terms below are defined to aid in understanding the descriptionsthat follow. The examples given within this section are for purposes ofillustration and not limitation.

A “clickstream” is a (possibly incomplete) sequence of content that hasbeen requested by a customer from a network site.

A “content item” may be any set of information that is accessible via anetwork. Examples can include a news story, a banner advertisement, agroup of mpeg movies, an audio track, a list of books, and so on. Acontent item may be displayed after a request to one or more networkaddresses. Determining what network address to assign to a content itemon is a subject of this patent. Let c_(i) be notation to designate theith content item on the site.

The “location” of a content item (or network address) may be the numberof clicks it takes, on average, for a customer to reach that contentitem (or network address), after the customer's first appearance on thesite during a session. For example, if the average customer firstencounters the search page on the fifth (5^(th)) click of his or herclickstream, the page would have a real-estate location of five (5).Note that the number of clicks does not take into account the particularpath taken, which may be different in each case. Let L(c_(i)) be used asa symbol to denote the location of content c_(i).

A “network” may be an interconnected set of server and client computersover a public or private communications medium (e.g., Internet,Arpanet).

A “network activity log” is a database, file, or other storage mediumthat records user activity on a network. Let X be notation to designatea set of all clickstreams in a network activity log.

A “network address” is a string that users may type or click to accessnetwork accessible information. Uniform Resource Locators (“URLs”) areexamples of network addresses. Multiple content items may be servedafter a request to a single network address. For example, differentcontent may appear within different frames on a page referenced by asingle network address. Let a_(j) be notation to designate the jthnetwork address on the site. Each content item must reside on one ormore network addresses.

A “network site” may be a collection of network addresses that may beserved to a requesting computer.

A “performance statistic” may be a measure of the effectiveness of acontent item in achieving business objectives. Examples of a performancestatistic may include the number of visitors requesting c_(i) per hour,the total revenue generated by c_(i) per day, the number of requests perhour for c_(i), the clickthrough rate (number of clicks divided bynumber of exposures) of visitors onto c_(i), profit generated by c_(i)per day, quantity of goods purchased in the session after requestingc_(i) per day, and so on. Let P(c_(i)) be used to denote the performanceof content item c_(i).

A “session” may be the complete clickstream (and associated client,server, and network information) of a visitor during a single visit at anetwork site. A session may begin when a server receives its firstrequest from a visitor, and end when there is 30 minutes or more ofinactivity from the visitor. The notation X_(i)εX will be used todesignate the ith session of the network activity log X.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, method, article, orapparatus. Further, unless expressly stated to the contrary, “or” refersto an inclusive or and not to an exclusive or. For example, a conditionA or B is satisfied by any one of the following: A is true (or present)and B is false (or not present), A is false (or not present) and B istrue (or present), and both A and B are true (or present).

Hardware and Software

Before discussing embodiments of the present invention, a hardwarearchitecture for using embodiments is described. FIG. 1 illustrates anexemplary architecture and includes a client computer 12 that isbi-directionally coupled to a network 14, and a server computer 16 thatis bi-directionally coupled to the network 14 and a database 18. Theclient computer 12 includes a central processing unit (“CPU”) 120, aread-only memory (“ROM”) 122, a random access memory (“RAM”) 124, a harddrive (“HD”) or storage memory 126, and input/output device(s) (“I/O”)128. The I/O 128 can include a keyboard, monitor, printer, electronicpointing device (e.g., mouse, trackball, etc.), or the like. The servercomputer 16 can include a CPU 160, ROM 162, RAM 164, HD 166, and I/O168.

Each of the client computer 12 and the server computer 16 is an exampleof a data processing system. ROM 122 and 162, RAM 124 and 164, HD 126and 166, and the database 18 include media that can be read by the CPU120 or 160. Therefore, each of these types of memories includes a dataprocessing system readable storage medium. These memories may beinternal or external to the computers 12 and 16.

The methods described herein may be implemented in suitable softwarecode that may reside within ROM 122 or 162, RAM 124 or 164, or HD 126 or166. In addition to those types of memories, the instructions in anembodiment of the present invention may be contained on a data storagedevice with a different data processing system readable storage medium,such as a floppy diskette. FIG. 2 illustrates a combination of softwarecode elements 204, 206, and 208 that are embodied within a dataprocessing system readable storage medium 202 on a HD 166.Alternatively, the instructions may be stored as software code elementson a DASD array, magnetic tape, floppy diskette, optical storage device,or other appropriate data processing system readable storage medium orstorage device.

In an illustrative embodiment of the invention, the computer-executableinstructions may be lines of compiled C⁺⁺, Java, or other language code.Other architectures may be used. For example, the functions of theclient computer 12 may be incorporated into the server computer 16, andvice versa. Further, other client computers (not shown) or other servercomputers (not shown) similar to client computer 12 and server computer16, respectively, may also be connected to the network 14. FIGS. 3 and 4include illustrations, in the form of flowcharts, of the structures andoperations of such software programs.

Communications between the client computer 12 and the server computer 16can be accomplished using electronic, optical, radio-frequency, or othersignals. When a user (human) is at the client computer 12, the clientcomputer 12 may convert the signals to a human understandable form whensending a communication to the user and may convert input from a humanto appropriate electronic, optical, radio-frequency, or other signals tobe used by the client computer 12 or the server computer 16.

The Network Activity Log

User behavior while “surfing” a network site may be collected intorepositories known as network activity logs. For example, if HypertextTransfer network Protocol (HTTP) were to be used, a user at a clientcomputer 12 may send a request for information in the form of a requestfor a network address over the network 14 to the server computer 16. Inresponse to the request, the server computer 16 sends informationcorresponding to the requested content over the network 14 to the clientcomputer 12 or information that the request could not be fulfilled(e.g., a “Page not found” error). Other users, similar to the user atclient computer 12, may be at other client computers and may also makerequests via the network 14 and server computer 16.

Whilst serving the requested content, the details of the user's requestmay be recorded in a network activity log (e.g., located within databaseor file 18). Network activity logs may record a range of informationincluding the date-of-request, time, bytes transferred, address ofrequesting computer, status code, and requestedcontent/page/file/network address.

For purposes of later elucidation, assume that the records from anetwork activity log have been placed into a table callednetwork_activity_log. Each row of the table is a request. The table mayhave the following columns: session, time, click_number, visitor, andcontent, where session is a code identifying a session, click_number isan integer greater than or equal to zero which is the number of requeststhat a user has made prior to the present record, visitor is a codeidentifying a visitor, and content is a code identifying a content item.

A session X_(i)εX contains the clickstream record of a visitor during asingle visit at a network site. Typically, a session begins when aserver receives its first request from a visitor (user) at clientcomputer 12, and ends when there is 30 minutes or more of inactivityfrom that same user. Session-determination (the process of assigningunique session ID numbers to each record) may be done in real-time bythe server computer 16 or may be done off-line after the network sitelog has been formed, when more CPU cycles are available to piecetogether customer behavior after the fact.

FIGS. 5 and 6 may depict example records from such a network activitylog. FIG. 5 may indicate that the user is participating in an auction.The user places a bid before leaving the site. FIG. 6 shows another userthat is seeking information about some “powertools.”

The methods to follow assume that a network activity log like the onedescribed above has been created. The network activity log will beanalyzed to determine how users are moving about on the site, and whereeach content item is “located” within the site.

Methodology

Once a network activity log has been obtained (block 302), fouractivities may be performed to determine the real-estate performance ofcontent items:

-   (i) Compute the location of each content item L(c_(i)) (block 322 in    FIGS. 3 and 4).-   (ii) Compute or access the actual performance of each content item    P(c_(i)) (block 424 in FIG. 4).-   (iii) Compute the predicted performance of each content item    P′(c_(i)) by analyzing the relationship between location and    performance in other content items (block 324 in FIG. 3).-   (iv) Decide whether each content item is under or over-performing or    substantially on par (blocks 342 and 444 of FIGS. 3 and 4,    respectively).-   (v) If desired, move one or more of the content items to a different    network address (blocks 362 and 462 of FIGS. 3 and 4, respectively).

Each of these activities will now be described in detail.

(i) Compute Each Content Item's Location

In one non-limiting embodiment, the location for a content item can be ameasure of how many clicks a typical user made to arrive at a specificnetwork address during a session at the network site regardless of path.For example, if the average customer first encounters the search networkaddress on the fifth (5^(th)) click of his or her clickstream, then thesearch page would have a real-estate location of five (5).

FIG. 7 includes an illustration of the location of “14V Drill” resultingfrom the clickpaths of three users on a hypothetical site. Although eachuser may take a different path, it is expected that a user willencounter “14V Drill” page after four (4) clicks. Therefore, thelocation of “14V Drill” may be referred to as being at the “4^(th)click”.

Another depiction of locations is shown in FIG. 8. Concentric rings showclick distances of 5 clicks, 10 clicks, and 15 clicks into theclickstream. “Arrivesite” is shown at the center, approximately oneclick into the customer's clickstream. “Leavesite” may occurapproximately 14 clicks later. “Freeoffers” may be requested atapproximately 11 clicks, “Index” may be requested at approximately 5clicks, and “search” on the main network address may be requestedapproximately 8 clicks later. “HAND TOOLS” is approximately 17 clicksfrom the arrival point, thus, the average user leaves (click distance ofapproximately 14 clicks) before reaching the hand tools network address(click distance of approximately 17 clicks).

Location may be written as

${L\left( c_{i} \right)} = {\frac{1}{T\left( c_{i} \right)}{\sum\limits_{X_{j} \in {X\text{:}c} \in X_{j}}^{\;}{\min\mspace{14mu}{{click}\left( {c_{i} \in X_{j}} \right)}}}}$whereclick(c_(i)) is the number of requests that a user made prior torequesting content C_(i); and T(c_(i)) may be the “traffic” or number ofsessions requesting content item c_(i) and may be written as:

${T\left( c_{i} \right)} = {\sum\limits_{X_{j} \in X}^{\;}{\left( {c_{i} \in X_{j}} \right).}}$

The method of summarizing a content item's “location” as the typicalencounter order of that content item in a visitor's session, is a novelaspect of this invention which has not been developed in prior art.

The following Structured Query Language (SQL) query code may be used tocompute location:

select session_data.content, avg(session_data.earliest_click) locationfrom (  select content, session,  min(click_number) earliest_click  fromnetwork_activity_log  group by content,session ) session_data group bysession_data.content(ii) Compute Each Content Item's Actual Performance

The actual performance of a content item P(c_(i)) can be found byobservation of the network activity log. For example, if the performancestatistic is the number of sessions requesting a content item or“traffic” (or T(c_(i))), then this may be computed by adding up thenumber of sessions requesting content c_(i) in the network activity log.

${P\left( c_{i} \right)} = {{T\left( c_{i} \right)} = {\sum\limits_{X_{j} \in X}^{\;}\left( {c_{i} \in X_{j}} \right)}}$

SQL code for computing traffic is described below.

  select content, count(distinct session) traffic fromnetwork_activity_log group by content(iii) Compute Each Content Item's Predicted Performance

A key idea of network real-estate analysis, is that content that isburied in the site should receive few requests because a user is likelyto leave before reaching it. Content that is near the main networkaddress or home page should receive more requests. The predictedperformance for c_(i) may, therefore, be computed as some functioninvolving L(c_(i)). In the example below, a spline function has beenparameterized to predict traffic performance, given knowledge ofL(c_(i)).

${P^{\prime}\left( c_{i} \right)} \approx {\sum\limits_{d = 1}^{D}\left\lbrack {{G\left( {{r_{d} - {L\left( c_{i} \right)}}} \right)} \cdot t_{d}} \right\rbrack}$where

-   D is the number of basis functions used for the approximation (it is    a parameter that may be estimated or fixed);-   G(g)=g²*log(g) (other functions are possible);-   r_(d) is the prototypical location value for the d^(th) basis    function; and-   t_(d) is a parameter value that is chosen to minimize the sum of    squared errors below over a large “training set” of content items

$\sum\limits_{a}{\left\lbrack {\left( {\sum\limits_{d = 1}^{D}{{G\left( {{r_{d} - {L\left( c_{i} \right)}}} \right)} \cdot t_{d}}} \right) - {P\left( c_{i} \right)}} \right\rbrack^{2}.}$(iv) Compare Actual and Predicted and Improve the Site

Predicted and actual values can be compared quantitatively or visually(blocks 342 of FIG. 3).

(iv-a) Quantitative Method

“Real-estate performance” or REP(c_(i)) may be defined as a measure ofthe actual performance of a content item compared to its predictedperformance. For example, REP may be defined as:

${{REP}\left( c_{i} \right)} = \frac{P\left( c_{i} \right)}{P^{\prime}\left( c_{i} \right)}$

This value may be used in a decision of whether to change the locationof a content item.

For example, content items with REP<1 may be demoted to poorer networkaddresses. Content items that are over-performing (REP>1) may bepromoted and displayed on network addresses with greater traffic.Similarly, high REP content items may be paired with high REP networkaddresses.

Referring to FIGS. 10 and 11, REP data from either or both figures canbe used to make a determination whether content should be moved. If so,the method can further comprise moving the content to a differentnetwork address (block 362 in FIG. 3).

Example of Quantitative Method

FIGS. 10 and 11 include information from the quantitative analysis. Thelast column (“Hits/E[Hits]”) is an example of REP, where the performancestatistic is “hits,” which may be the number of requests in the networkactivity log in total. These figures show that for the Frequent Buyers'Club (“FBC”), reconditioned tools, outdoor products, and gardenequipment all appear to be over-performers. FBC items have a very highREP of 3.1 and 1.9 for “/cpi/taf/fbc.taf|f=list” and“/cpi/taf/fbc.taf|-”, respectively. Reconditioned tools(“/cpi/taf/category.taf|-|RECONED” and “RECONED”) are only reached afterapproximately 17 clicks—they seem to be buried in the site—however, areattracting approximately 1.9 times the number of requests than would beexpected at that location (REP=1.9). These content items may be changedto network addresses higher in the hierarchy and could be displayedcloser to the main network address.

The shopping basket add, confirm, change, and associated actions (e.g.,“/cpi/taf/basket.taf|actionarg=add”), all show very high REP values(e.g., the above-mentioned content item has an REP of 10). The reasonfor these large REP values is because these actions are only performedafter a long period of continuous browsing on the site. Therefore, theyappear to be attracting a lot of activity for their location. A sitedesigner may examine these results and conclude that these content itemsprobably should not be moved or optimized. This example underscores thatit may be useful to have a human site designer interpret and understandthe real-estate analysis results before taking actions to optimize thesite.

The worst performers are the AboutUs and Auction screens(“/cpi/html/aboutus/main.htm|-” and “/cpi/taf/auction.taf|f=loginform”,respectively). Auction login is situated only 9 clicks from thearrive-site address, however, has an REP of 0.01. This is 100 timesfewer requests than other content in this location. Perhaps only a smallnumber of customers actually have an auction account, and therefore, canlogin to auctions. In that case it may be moved off the main companypage.

(iv-b) Visual Method

If the independent variable is location, then an alternative, graphicalmethod may be employed. Under this embodiment, the method can involvecomputing the performance and location for various a content items,generating a graph relating location to performance, and reviewing thegraph (blocks 424, 442, and 446 of FIG. 4). A curve may be super-imposedonto the graph for ease of readability (block 444). Using this graph,assessment can be made as to which content items should be promoted ordemoted based on whether those content items appear significantly abovethe curve/preponderance of the points (in which case they should beconsidered for promotion) or significantly below the curve/preponderanceof the points (in which case they should be considered for demotion).Content items close to the curve/preponderance of points may remain attheir locations (e.g., insignificant difference between actual andpredicted performance).

Example of Visual Method

FIG. 9 can be an example of such a graph and includes a semi-log plotwith click distance (linear scale) along the x-axis and traffic(logarithmic scale) along the y-axis. The plot has some significant anduseful information for the network site in its current configuration.Line 142 approximates the expected maximum traffic for a given clickdistance. Line 144 indicates a minimum amount of traffic is seen at mostnetwork addresses regardless of click distance.

Cluster 146 includes some content items that are typically requestedearly in a session with traffic close to line 144. The content withincluster 146 may be closer to a typical arrive-site network address thanthey should be. On the other end of the spectrum, content items 148 and149 are requested significantly higher than would be predicted by theirreal-estate location. Content item 148 has an amount of traffic is aboutthe same as an arrive-site network address. Content item 149 iscurrently at a location of approximately 29 clicks and has traffic thatwould correspond to a click distance of approximately 21 clicks.

A site designer may want to further investigate to determine if thetraffic seen at network addresses within cluster 146 and content items148 and 149 can be explained. For example, content item 148 may be on alanding network address that is accessed from an affiliated web site.This may explain why its traffic is high. Content item 148 may not bemoved because its location may be logical within the layout of thenetwork site.

One or more of the content items may be moved to a different networkaddress (blocks 362 and 462 of FIGS. 3 and 4, respectively) with theobjective of improving the overall site design. Some of the contentitems within cluster 146 may be moved to other network addresses withhigher location scores, and content item 149 may be moved closer to anetwork address with a lower location score. Clearly other actions couldbe taken for the other content items shown in FIG. 9.

Other Embodiments

Many other embodiments are possible. For example, the concentric graphshown in FIG. 8 may be used. An unusually high performance for a contentitem near the outer portions of the concentric graph may signal that theitem should be moved.

The methods described above can be performed at least on part on clientcomputer 12, the server computer 16, or other computer (not shown). Forthe computers, a data readable storage medium can include code embodiedtherein, wherein the code includes instructions for carrying out acts ofthe method. A site designer may not want the computer to automaticallymove the content items because control over the network site may be lostor the content items may be placed at locations that are not logical tousers at client computer 12. Still, the code can include an instructionfor recommending that the particular content items be moved to adifferent location. For the performance statistic being investigated, acomputer may recommend that a particular content item be moved to alocation that is a different click distance from a reference contentitem such as the “index” page. Because the design of the network siteshould be cohesive, the site designer may be better able to review therecommendation of the computer to actuate a change if the site designerso desires.

In other embodiments, the approximations recited above may be replacedby equations. In other embodiments, the frame of reference for movingaddressed from a fixed reference point, such as a main network siteaddress.

In the foregoing specification, the invention has been described withreference to specific embodiments. However, one of ordinary skill in theart appreciates that various modifications and changes can be madewithout departing from the scope of the present invention as set forthin the claims below. Accordingly, the specification and figures are tobe regarded in an illustrative rather than a restrictive sense, and allsuch modifications are intended to be included within the scope ofpresent invention.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature or element of any or all the claims.

1. A method for analyzing performance of content items within a networksite, comprising: at a server computer having access to network activityinformation on the network site: determining a location of a contentitem within the network site; determining a predicted performance forthe content item at the location; determining an actual performance ofthe content item; comparing the predicted performance to the actualperformance; based on the comparing, determining whether the contentitem is underperforming; and moving the content item to a new locationwithin the network site responsive to the determining whether thecontent item is underperforming.
 2. The method according to claim 1,wherein determining the predicted performance of the content itemcomprises analyzing a relationship between location and performance ofother content items.
 3. The method of claim 1, wherein determining thelocation of the content item comprises determining an average number ofclicks it takes users to arrive at specific network address during asession at the network site.
 4. The method of claim 1, whereindetermining the actual performance of the content item comprises summingthe number of session in which the content item is requested.
 5. Themethod of claim 1, wherein the predicted performance of the content itemis determined according to:${P^{\prime}\left( c_{i} \right)} \approx {\sum\limits_{d = 1}^{D}\left\lbrack {{G\left( {{r_{d} - {L\left( c_{i} \right)}}} \right)} \cdot t_{d}} \right\rbrack}$where: c_(i) represents the content item; L(c_(i)) is the location ofthe content item; D is a number of basis functions used for theapproximation G(g)=g²*log(g); r_(d) is a prototypical location value forthe d^(th) basis function; and t_(d) is a parameter value that is chosento minimize the sum of squared errors below over a large “training set”of content items.
 6. The method according to claim 2, wherein comparingthe actual performance to the predicted performance comprisesdetermining a real estate performance value according to:${{REP}\left( c_{i} \right)} = {\frac{P\left( c_{i} \right)}{P^{\prime}\left( c_{i} \right)}.}$7. The method according to claim 6, wherein if the content item has aREP<1, the content item is determined to be underperforming.
 8. A dataprocessing system readable storage medium having computer readableprogram code embodied therein, the computer readable program codeadapted to be executed by a computer to implement a method for analyzingperformance of content items within a network site, the computerreadable program code comprising instructions executable to: determine apredicted performance for the content item at the location; determine anactual performance of the content item; compare the predictedperformance to the actual performance; based on the comparison,determine if the content item is underperforming; and if the contentitem is underperforming, recommend moving the content item to a newlocation within the network site.
 9. The data processing system of claim8, wherein determining the predicted performance of the content itemcomprises analyzing a relationship between location and performance ofother content items.
 10. The data processing system of claim 8, whereindetermining the location of the content item comprises determining anaverage number of clicks it takes users to arrive at a specific networkaddress during a session at the network site.
 11. The data processingsystem of claim 8, wherein determining the actual performance of thecontent item comprises summing the number of sessions in which thecontent item is requested.
 12. The data processing system of claim 8,wherein the predicted performance of the content item is determinedaccording to:${P^{\prime}\left( c_{i} \right)} \approx {\sum\limits_{d = 1}^{D}\left\lbrack {{G\left( {{r_{d} - {L\left( c_{i} \right)}}} \right)} \cdot t_{d}} \right\rbrack}$where: c_(i) represents the content item; L(c_(i)) is the location ofthe content item; D is a number of basis functions used for theapproximation G(g)=g²*log(g); r_(d) is a prototypical location value forthe d^(th) basis function; and t_(d) is a parameter value that is chosento minimize the sum of squared errors below over a large “training set”of content items.
 13. The data processing system of claim 12, whereincomparing the actual performance to the predicted performance comprisesdetermining a real estate performance value according to:${{REP}\left( c_{i} \right)} = {\frac{P\left( c_{i} \right)}{P^{\prime}\left( c_{i} \right)}.}$14. The data processing system of claim 8, wherein if the content itemhas a REP<1, the content item is determined to be underperforming.
 15. Adata processing system for analyzing performance of content items withina network site, comprising: a storage location storing network activityinformation on the network site; and a server computer coupled to thestorage location comprising a processor and a computer readable storagemedium storing computer executable instruction, the computer executableinstructions comprising instructions executable to: determine apredicted performance for the content item at the location; determine anactual performance of the content item; compare the predictedperformance to the actual performance; based on the comparison,determine if the content item is underperforming; and if the contentitem is underperforming, recommend moving the content item to a newlocation within the network site.
 16. The data processing system ofclaim 15, wherein determining the predicted performance of the contentitem comprises analyzing a relationship between location and performanceof other content items.
 17. The data processing system of claim 15,wherein determining the location of the content item comprisesdetermining an average number of clicks it takes users to arrive at aspecific network address during a session at the network site.
 18. Thedata processing system of claim 15, wherein determining the actualperformance of the content item comprises summing the number of sessionsin which the content item is requested.
 19. The data processing systemof claim 15, wherein the predicted performance of the content item isdetermined according to:${P^{\prime}\left( c_{i} \right)} \approx {\sum\limits_{d = 1}^{D}\left\lbrack {{G\left( {{r_{d} - {L\left( c_{i} \right)}}} \right)} \cdot t_{d}} \right\rbrack}$where: c_(i) represents the content item; L(c_(i)) is the location ofthe content item; D is a number of basis functions used for theapproximation G(g)=g²*log(g); r_(d) is a prototypical location value forthe d^(th) basis function; and t_(d) is a parameter value that is chosento minimize the sum of squared errors below over a large “training set”of content items.
 20. The data processing system of claim 19, whereincomparing the actual performance to the predicted performance comprisesdetermining a real estate performance value according to:${{REP}\left( c_{i} \right)} = {\frac{P\left( c_{i} \right)}{P^{\prime}\left( c_{i} \right)}.}$21. The data processing system of claim 15, wherein if the content itemhas a REP<1, the content item is determined to be underperforming.